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cs.CV2026

InstanceControl: Controllable Complex Image Generation without Instance Labeling

Xiaoyu Liu, Huan Wang, Fan Li +4

Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. Howev…

cs.CV2026

Illuminating Unified Multimodal Model for Free-form Interleaved Text-Image Generation

Chonghuinan Wang, Zhikai Chen, Chunwei Wang +9

The advancement of generative AI models capable of producing text and image marks a critical step forward in the realm of multimodal intelligence, particularly for tasks involving…

cs.CV2026

ShotCrop: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions

Dehong Kong, Lina Lei, Lingtao Zheng +10

Prior work on aesthetic composition typically produces a single aesthetically pleasing crop, overlooking the narrative value of composing multiple shots from one scene. In practice…

cs.CV2026

Fast Image Super-Resolution via Consistency Rectified Flow

Jiaqi Xu, Wenbo Li, Haoze Sun +8

Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step sampling largely hinders thei…

cs.CV2026

HP-Edit: A Human-Preference Post-Training Framework for Image Editing

Fan Li, Chonghuinan Wang, Lina Lei +9

Common image editing tasks typically adopt powerful generative diffusion models as the leading paradigm for real-world content editing. Meanwhile, although reinforcement learning (…

cs.CV2026

ColorFLUX: A Structure-Color Decoupling Framework for Old Photo Colorization

Bingchen Li, Zhixin Wang, Fan Li +5

Old photos preserve invaluable historical memories, making their restoration and colorization highly desirable. While existing restoration models can address some degradation issue…

cs.CV2026

PlanViz: Evaluating Planning-Oriented Image Generation and Editing for Computer-Use Tasks

Junxian Li, Kai Liu, Leyang Chen +7

Unified multimodal models (UMMs) have shown impressive capabilities in complex natural image generation. However, their potential in computer-use planning tasks, which are closely…

cs.CV2025

Test-Time Preference Optimization for Image Restoration

Bingchen Li, Xin Li, Jiaqi Xu +4

Image restoration (IR) models are typically trained to recover high-quality images using L1 or LPIPS loss. To handle diverse unknown degradations, zero-shot IR methods have also be…

cs.CV2025

PocketSR: The Super-Resolution Expert in Your Pocket Mobiles

Haoze Sun, Linfeng Jiang, Fan Li +9

Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such as those captured by mobile phones. While existing methods leveraging larg…

cs.CV2025

Dual Prompting Image Restoration with Diffusion Transformers

Dehong Kong, Fan Li, Zhixin Wang +4

Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due t…

cs.CV2025

Turbo2K: Towards Ultra-Efficient and High-Quality 2K Video Synthesis

Jingjing Ren, Wenbo Li, Zhongdao Wang +9

Demand for 2K video synthesis is rising with increasing consumer expectations for ultra-clear visuals. While diffusion transformers (DiTs) have demonstrated remarkable capabilities…